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Скачать или смотреть Extracting Files from a Specific Date Range Using grep, awk, or Python

  • vlogize
  • 2025-08-25
  • 2
Extracting Files from a Specific Date Range Using grep, awk, or Python
Extract Only Lines Of 4 days using grep or awkpythonphpunixawkgrep
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Описание к видео Extracting Files from a Specific Date Range Using grep, awk, or Python

Learn how to easily extract files from a specific date range using command-line tools or Python. We'll guide you step-by-step, ensuring clarity for both beginners and experts.
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This video is based on the question https://stackoverflow.com/q/64276731/ asked by the user 'Am3Y' ( https://stackoverflow.com/u/14393867/ ) and on the answer https://stackoverflow.com/a/64277097/ provided by the user 'Amiral ASTERO' ( https://stackoverflow.com/u/14414260/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Extract Only Lines Of 4 days using grep or awk

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The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

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How to Extract Files from the Last 4 Days Using grep, awk, or Python

Managing and sorting through large volumes of text files can be a daunting task, especially when they contain entries with date-based naming conventions. If you find yourself needing to extract filenames that fall within a particular date range, this guide will walk you through the process of accomplishing this using either command-line tools like grep and awk, or a straightforward Python script.

The Problem: Selecting Files Within a Specific Date Range

Imagine you've got a text file filled with database backups, and you want to filter this list to show only the files that are from the last 4 days. Here's what your file might look like:

[[See Video to Reveal this Text or Code Snippet]]

The goal is to extract files from the current date minus 4 days to any older date within the file. Fortunately, since these filenames all follow a YYYY-MM-DD naming convention, achieving this through simple string comparisons is straightforward.

Solution Overview

Using Python

The first solution we'll explore uses a simple Python script. If you're comfortable with Python, this is likely the best approach as it yields a clear and readable result.

Step-by-Step Implementation

Import Required Libraries: You'll need datetime and timedelta for date handling.

Set Up Your Filename: Replace 'your_file' with the actual file name containing your entries.

Calculate Date Range: Create a start date that represents today minus 4 days.

Read the File: Load all entries from the file into a list.

Filter Entries: Use list comprehension to filter out the relevant files.

Here’s a sample script to guide you through:

[[See Video to Reveal this Text or Code Snippet]]

Using grep or awk

If you prefer to work directly from the command line, tools like grep or awk can be extremely useful. However, string comparison for dates in filenames can be complex and typically requires more nuanced commands.

Example Using awk

For an awk command, you'll need to adjust the script to properly filter filenames based on dates, which may require additional setup depending on your actual requirements.

Here’s a simple command structure to get you started:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

No matter which method you choose, extracting files based on their date from a formatted list is entirely possible with the right approach. Whether through Python's flexibility and clarity, or the power of command-line tools, you can efficiently manipulate and manage your file names to find exactly what you need.

Now you can confidently filter through your databases, ensuring that you’re always working with the most relevant data. Happy coding!

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